One of the problems is that wearables are mainly bought by affluent, healthy individuals. A recent report by industry analysts NPD found that 41% of people who owned fitness trackers had an average income of over $100,000. The people most affected by poor health often don’t have resources to benefit from new technology. Even if they did, their data doesn’t integrate with their care provider and their doctor doesn’t know what to do with the information.

Wearable devices often come with digital behavior change interventions, designed to help people self-manage. But from the number of wearables for sale on eBay it seems the long term utility of these devices is limited. One report found that up to one third of Americans who bought a wearable tech product gave up on it within six months. Smartphones and laptops don’t have this kind of drop-off.

So why is this happening and what will be the most successful application for wearable technology? One answer could be that the wearables aren’t really solving a problem. They are a ‘nice-to-have.’ The additional benefit of tracking exercise or steps is so marginal it is not worth maintaining in the long run.

People with long term conditions, by contrast, do have needs that need addressing. They can face daily challenges such as getting quick access to care or monitoring their disease to titrate medications. Mainstream wearable technology does little to address these challenges. There are some specialized examples, like the Empatica wristband. It helps people with epilepsy predict and detect the onset of seizures.

As well as specialized devices, data gathered from generic fitness trackers and smartphones could be used to detect relapse in a chronic condition. Researchers have used data such as activity, screen lock events, light and ambient sound sensors, Bluetooth or Wifi connections to predict disease states in a variety of conditions. A number of start ups have emerged, like Ginger.io, working to integrate these insights into the health system. But so far, these companies and researchers have had little success in bringing this data into clinical practice.

Three big problems remain:

The evidence is limited: most studies are small — 20–40 people. Clinicians can’t and won’t recommend the use of a device that could trigger false alarms, or miss a dangerous relapse.

The data doesn’t integrate: each clinician manages multiple patients and does not have time to log into dashboards for each wearable device.

Clinicians don’t know what to do with the information: In general medicine is reactive not proactive. There are guidelines for when someone is already sick, but nothing to tell a clinician what to do if the person is predicted to get sick in the next few days.

A new €22m research project is trying to change this. RADAR-CNS (Remote Assessment of Disease and Relapse — Central Nervous System) is a collaboration between academic research centers and companies across Europe.

The aim is to find predictors of relapse in depression, multiple sclerosis and epilepsy using smartphone and wearable data.

The consortium will target each of these three problems over a five year period. They will conduct large clinical trials using smartphone and wearable data to find reliable predictors of relapse that work across any device. In the process they aim to build an architecture that allows a patient to bring their own device to their clinician to extract meaningful insights from the data. Towards the end of the project they will work with clinicians to map out treatment plans and pathways using the new data, helping them work out what to do with the new insights.

The wearable tech hype is cooling off. Now, projects like RADAR-CNS are steadily working on equipping health care systems to make proper use of these new data streams.